Overview

Dataset statistics

Number of variables12
Number of observations265471
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.3 MiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

df_index is highly correlated with IDHigh correlation
ID is highly correlated with df_indexHigh correlation
u is highly correlated with g and 6 other fieldsHigh correlation
g is highly correlated with u and 8 other fieldsHigh correlation
r is highly correlated with u and 8 other fieldsHigh correlation
i is highly correlated with u and 7 other fieldsHigh correlation
z is highly correlated with u and 7 other fieldsHigh correlation
uErr is highly correlated with u and 4 other fieldsHigh correlation
gErr is highly correlated with u and 8 other fieldsHigh correlation
rErr is highly correlated with u and 8 other fieldsHigh correlation
iErr is highly correlated with g and 6 other fieldsHigh correlation
zErr is highly correlated with g and 6 other fieldsHigh correlation
df_index is highly correlated with IDHigh correlation
ID is highly correlated with df_indexHigh correlation
u is highly correlated with g and 3 other fieldsHigh correlation
g is highly correlated with u and 3 other fieldsHigh correlation
r is highly correlated with u and 3 other fieldsHigh correlation
i is highly correlated with u and 3 other fieldsHigh correlation
z is highly correlated with u and 3 other fieldsHigh correlation
uErr is highly correlated with gErr and 1 other fieldsHigh correlation
gErr is highly correlated with uErr and 1 other fieldsHigh correlation
rErr is highly correlated with uErr and 1 other fieldsHigh correlation
df_index is highly correlated with IDHigh correlation
ID is highly correlated with df_indexHigh correlation
u is highly correlated with g and 2 other fieldsHigh correlation
g is highly correlated with u and 5 other fieldsHigh correlation
r is highly correlated with g and 6 other fieldsHigh correlation
i is highly correlated with g and 6 other fieldsHigh correlation
z is highly correlated with g and 6 other fieldsHigh correlation
uErr is highly correlated with u and 1 other fieldsHigh correlation
gErr is highly correlated with u and 7 other fieldsHigh correlation
rErr is highly correlated with g and 6 other fieldsHigh correlation
iErr is highly correlated with r and 5 other fieldsHigh correlation
zErr is highly correlated with r and 4 other fieldsHigh correlation
df_index is highly correlated with IDHigh correlation
ID is highly correlated with df_indexHigh correlation
u is highly correlated with g and 6 other fieldsHigh correlation
g is highly correlated with u and 6 other fieldsHigh correlation
r is highly correlated with u and 6 other fieldsHigh correlation
i is highly correlated with u and 3 other fieldsHigh correlation
z is highly correlated with u and 3 other fieldsHigh correlation
uErr is highly correlated with u and 6 other fieldsHigh correlation
gErr is highly correlated with u and 6 other fieldsHigh correlation
rErr is highly correlated with u and 6 other fieldsHigh correlation
iErr is highly correlated with uErr and 3 other fieldsHigh correlation
zErr is highly correlated with uErr and 3 other fieldsHigh correlation
uErr is highly skewed (γ1 = 441.8980897) Skewed
gErr is highly skewed (γ1 = 407.9554013) Skewed
rErr is highly skewed (γ1 = 115.6323666) Skewed
iErr is highly skewed (γ1 = 137.6867747) Skewed
zErr is highly skewed (γ1 = 40.41128375) Skewed
ID has unique values Unique

Reproduction

Analysis started2022-02-24 03:57:30.218880
Analysis finished2022-02-24 03:58:03.347150
Duration33.13 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct74950
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36057.1363
Minimum0
Maximum74949
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:03.385503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3704
Q118433
median35819
Q353240
95-th percentile70011.5
Maximum74949
Range74949
Interquartile range (IQR)34807

Descriptive statistics

Standard deviation20796.5678
Coefficient of variation (CV)0.5767670406
Kurtosis-1.109257609
Mean36057.1363
Median Absolute Deviation (MAD)17404
Skewness0.0508676695
Sum9572124031
Variance432497232.1
MonotonicityNot monotonic
2022-02-24T00:58:03.478720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374754
 
< 0.1%
373694
 
< 0.1%
373454
 
< 0.1%
373464
 
< 0.1%
602674
 
< 0.1%
373484
 
< 0.1%
373494
 
< 0.1%
373504
 
< 0.1%
373514
 
< 0.1%
373524
 
< 0.1%
Other values (74940)265431
> 99.9%
ValueCountFrequency (%)
03
< 0.1%
14
< 0.1%
23
< 0.1%
34
< 0.1%
43
< 0.1%
53
< 0.1%
63
< 0.1%
73
< 0.1%
84
< 0.1%
94
< 0.1%
ValueCountFrequency (%)
749491
< 0.1%
749481
< 0.1%
749471
< 0.1%
749461
< 0.1%
749451
< 0.1%
749441
< 0.1%
749431
< 0.1%
749421
< 0.1%
749411
< 0.1%
749401
< 0.1%

ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct265471
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.23766412 × 1018
Minimum1.23764588 × 1018
Maximum1.237680531 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:03.572470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.23764588 × 1018
5-th percentile1.23765125 × 1018
Q11.237657192 × 1018
median1.237663239 × 1018
Q31.237668584 × 1018
95-th percentile1.237679438 × 1018
Maximum1.237680531 × 1018
Range3.465179365 × 1013
Interquartile range (IQR)1.139238011 × 1013

Descriptive statistics

Standard deviation9.180408119 × 1012
Coefficient of variation (CV)7.417527883 × 10-6
Kurtosis-0.8589275398
Mean1.23766412 × 1018
Median Absolute Deviation (MAD)5.651643696 × 1012
Skewness0.3502992022
Sum8.972874912 × 1018
Variance8.427989322 × 1025
MonotonicityNot monotonic
2022-02-24T00:58:03.666221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.23764588 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
1.237666339 × 10181
 
< 0.1%
Other values (265461)265461
> 99.9%
ValueCountFrequency (%)
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
ValueCountFrequency (%)
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%

u
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct256277
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.4897471
Minimum6.137899
Maximum31.474758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:03.775585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.137899
5-th percentile18.790246
Q121.248047
median22.690958
Q323.9247045
95-th percentile25.694047
Maximum31.474758
Range25.336859
Interquartile range (IQR)2.6766575

Descriptive statistics

Standard deviation2.0892633
Coefficient of variation (CV)0.09289847906
Kurtosis-0.07758833777
Mean22.4897471
Median Absolute Deviation (MAD)1.314068
Skewness-0.403543113
Sum5970375.652
Variance4.365021136
MonotonicityNot monotonic
2022-02-24T00:58:03.931846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.7848284
 
< 0.1%
22.601414
 
< 0.1%
21.962664
 
< 0.1%
23.6093254
 
< 0.1%
24.324493
 
< 0.1%
22.0595613
 
< 0.1%
23.7900313
 
< 0.1%
22.6558763
 
< 0.1%
22.116983
 
< 0.1%
22.1298293
 
< 0.1%
Other values (256267)265437
> 99.9%
ValueCountFrequency (%)
6.1378991
< 0.1%
7.6844861
< 0.1%
7.858441
< 0.1%
8.1079041
< 0.1%
8.1748131
< 0.1%
8.2236691
< 0.1%
9.0463741
< 0.1%
9.4453431
< 0.1%
9.5993571
< 0.1%
9.6802211
< 0.1%
ValueCountFrequency (%)
31.4747581
< 0.1%
30.6697851
< 0.1%
30.0455911
< 0.1%
30.0295751
< 0.1%
29.9149651
< 0.1%
29.5847641
< 0.1%
29.4974861
< 0.1%
29.228761
< 0.1%
29.1903691
< 0.1%
28.9785141
< 0.1%

g
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253305
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.9485549
Minimum7.446142
Maximum32.311321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:04.009970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7.446142
5-th percentile17.3354195
Q119.753766
median21.534155
Q322.319359
95-th percentile23.350527
Maximum32.311321
Range24.865179
Interquartile range (IQR)2.565593

Descriptive statistics

Standard deviation1.944626527
Coefficient of variation (CV)0.09282867181
Kurtosis-0.01322663839
Mean20.9485549
Median Absolute Deviation (MAD)1.02001
Skewness-0.7648934951
Sum5561233.817
Variance3.781572331
MonotonicityNot monotonic
2022-02-24T00:58:04.103720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.3623434
 
< 0.1%
22.0555024
 
< 0.1%
21.6922044
 
< 0.1%
22.8920044
 
< 0.1%
18.4767424
 
< 0.1%
22.5216314
 
< 0.1%
21.9363274
 
< 0.1%
22.474764
 
< 0.1%
21.2525293
 
< 0.1%
22.0107823
 
< 0.1%
Other values (253295)265433
> 99.9%
ValueCountFrequency (%)
7.4461421
< 0.1%
8.2411271
< 0.1%
8.6855511
< 0.1%
8.8542821
< 0.1%
8.8799681
< 0.1%
9.0436551
< 0.1%
9.5410521
< 0.1%
9.9554121
< 0.1%
10.049981
< 0.1%
10.2044561
< 0.1%
ValueCountFrequency (%)
32.3113211
< 0.1%
32.1803591
< 0.1%
32.1802181
< 0.1%
31.121991
< 0.1%
31.0367241
< 0.1%
30.374721
< 0.1%
30.2740631
< 0.1%
30.0042611
< 0.1%
29.9160211
< 0.1%
29.6989311
< 0.1%

r
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253330
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.71917763
Minimum8.510301
Maximum30.481144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:04.197470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8.510301
5-th percentile16.5637895
Q118.49573
median20.201982
Q320.99195
95-th percentile22.0363685
Maximum30.481144
Range21.970843
Interquartile range (IQR)2.49622

Descriptive statistics

Standard deviation1.75713942
Coefficient of variation (CV)0.0891081491
Kurtosis-0.1970252936
Mean19.71917763
Median Absolute Deviation (MAD)1.0718
Skewness-0.6758215825
Sum5234869.805
Variance3.087538942
MonotonicityNot monotonic
2022-02-24T00:58:04.275595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.3979435
 
< 0.1%
20.4314695
 
< 0.1%
20.1583375
 
< 0.1%
20.877315
 
< 0.1%
20.889254
 
< 0.1%
20.3695774
 
< 0.1%
20.5947214
 
< 0.1%
20.2153154
 
< 0.1%
20.9701864
 
< 0.1%
21.4459364
 
< 0.1%
Other values (253320)265427
> 99.9%
ValueCountFrequency (%)
8.5103011
< 0.1%
8.8714521
< 0.1%
9.3814331
< 0.1%
9.5374031
< 0.1%
9.544841
< 0.1%
9.8061571
< 0.1%
9.8710261
< 0.1%
10.185641
< 0.1%
10.6857151
< 0.1%
10.7132711
< 0.1%
ValueCountFrequency (%)
30.4811441
< 0.1%
27.929351
< 0.1%
27.5865121
< 0.1%
27.5316031
< 0.1%
27.4438321
< 0.1%
27.3402421
< 0.1%
26.7601411
< 0.1%
26.3037871
< 0.1%
26.2987371
< 0.1%
26.2759911
< 0.1%

i
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253727
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.1421622
Minimum9.260902
Maximum32.286316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:04.369345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9.260902
5-th percentile16.1717205
Q117.974121
median19.415615
Q320.274704
95-th percentile21.7420865
Maximum32.286316
Range23.025414
Interquartile range (IQR)2.300583

Descriptive statistics

Standard deviation1.729917104
Coefficient of variation (CV)0.09037208472
Kurtosis0.03961488273
Mean19.1421622
Median Absolute Deviation (MAD)1.0865
Skewness-0.3474433238
Sum5081688.942
Variance2.992613187
MonotonicityNot monotonic
2022-02-24T00:58:04.463083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.472084
 
< 0.1%
19.3645784
 
< 0.1%
19.7024484
 
< 0.1%
19.5749934
 
< 0.1%
19.6772334
 
< 0.1%
19.3864274
 
< 0.1%
19.9620574
 
< 0.1%
19.7688394
 
< 0.1%
19.5043454
 
< 0.1%
19.2633064
 
< 0.1%
Other values (253717)265431
> 99.9%
ValueCountFrequency (%)
9.2609021
< 0.1%
9.454091
< 0.1%
9.4813671
< 0.1%
9.7858241
< 0.1%
10.004291
< 0.1%
10.2100691
< 0.1%
10.4990231
< 0.1%
10.6018751
< 0.1%
10.6670531
< 0.1%
11.0984521
< 0.1%
ValueCountFrequency (%)
32.2863161
< 0.1%
31.5754361
< 0.1%
31.3508721
< 0.1%
31.2541241
< 0.1%
31.1561531
< 0.1%
31.130891
< 0.1%
31.0734921
< 0.1%
30.8731121
< 0.1%
30.7200781
< 0.1%
30.7095891
< 0.1%

z
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253767
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.81974907
Minimum9.688597
Maximum29.146568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:04.556837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9.688597
5-th percentile15.876671
Q117.6424865
median19.002357
Q319.89907
95-th percentile21.6314295
Maximum29.146568
Range19.457971
Interquartile range (IQR)2.2565835

Descriptive statistics

Standard deviation1.757823963
Coefficient of variation (CV)0.09340315623
Kurtosis0.04937809918
Mean18.81974907
Median Absolute Deviation (MAD)1.082673
Skewness-0.1689879989
Sum4996097.605
Variance3.089945084
MonotonicityNot monotonic
2022-02-24T00:58:04.650583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2144744
 
< 0.1%
19.5186964
 
< 0.1%
19.1545524
 
< 0.1%
19.3487784
 
< 0.1%
19.2798484
 
< 0.1%
19.1961154
 
< 0.1%
18.9476574
 
< 0.1%
18.8589744
 
< 0.1%
18.7925114
 
< 0.1%
19.0099914
 
< 0.1%
Other values (253757)265431
> 99.9%
ValueCountFrequency (%)
9.6885971
< 0.1%
10.1116691
< 0.1%
10.1381
< 0.1%
10.2461931
< 0.1%
10.443391
< 0.1%
10.6679351
< 0.1%
10.6775681
< 0.1%
10.7370981
< 0.1%
10.839861
< 0.1%
10.8814441
< 0.1%
ValueCountFrequency (%)
29.1465681
< 0.1%
29.1050491
< 0.1%
28.9548611
< 0.1%
28.8603271
< 0.1%
28.7417531
< 0.1%
28.713531
< 0.1%
28.688851
< 0.1%
28.6705991
< 0.1%
28.6201741
< 0.1%
28.6153451
< 0.1%

uErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct232977
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.506739932
Minimum0.011919
Maximum973.115381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:04.744468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.011919
5-th percentile0.0407055
Q10.1616275
median0.421703
Q30.738313
95-th percentile1.2357135
Maximum973.115381
Range973.103462
Interquartile range (IQR)0.5766855

Descriptive statistics

Standard deviation1.992068476
Coefficient of variation (CV)3.931145644
Kurtosis214288.0944
Mean0.506739932
Median Absolute Deviation (MAD)0.284124
Skewness441.8980897
Sum134524.7565
Variance3.968336815
MonotonicityNot monotonic
2022-02-24T00:58:04.838210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.046596
 
< 0.1%
0.0940356
 
< 0.1%
0.0681756
 
< 0.1%
0.0633066
 
< 0.1%
0.0394196
 
< 0.1%
0.0985496
 
< 0.1%
0.0713525
 
< 0.1%
0.0649555
 
< 0.1%
0.0533025
 
< 0.1%
0.0712165
 
< 0.1%
Other values (232967)265415
> 99.9%
ValueCountFrequency (%)
0.0119191
< 0.1%
0.0122591
< 0.1%
0.0126041
< 0.1%
0.0130491
< 0.1%
0.0132491
< 0.1%
0.0135271
< 0.1%
0.0136171
< 0.1%
0.0136561
< 0.1%
0.0137251
< 0.1%
0.013781
< 0.1%
ValueCountFrequency (%)
973.1153811
< 0.1%
163.3046491
< 0.1%
115.0031151
< 0.1%
86.1842531
< 0.1%
70.9041981
< 0.1%
61.4911781
< 0.1%
53.8911291
< 0.1%
40.4335751
< 0.1%
22.0307311
< 0.1%
17.7753921
< 0.1%

gErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct171592
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1677167151
Minimum0.021987
Maximum708.703847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:04.994469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.021987
5-th percentile0.030645
Q10.0527855
median0.124564
Q30.20737
95-th percentile0.428923
Maximum708.703847
Range708.68186
Interquartile range (IQR)0.1545845

Descriptive statistics

Standard deviation1.506463924
Coefficient of variation (CV)8.982193114
Kurtosis186335.3772
Mean0.1677167151
Median Absolute Deviation (MAD)0.075263
Skewness407.9554013
Sum44523.92408
Variance2.269433553
MonotonicityNot monotonic
2022-02-24T00:58:05.072594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03310813
 
< 0.1%
0.0324212
 
< 0.1%
0.03783711
 
< 0.1%
0.03467611
 
< 0.1%
0.03301411
 
< 0.1%
0.03817211
 
< 0.1%
0.03315711
 
< 0.1%
0.03037411
 
< 0.1%
0.03420411
 
< 0.1%
0.0334811
 
< 0.1%
Other values (171582)265358
> 99.9%
ValueCountFrequency (%)
0.0219871
< 0.1%
0.0221271
< 0.1%
0.0224391
< 0.1%
0.0224881
< 0.1%
0.0225521
< 0.1%
0.0225941
< 0.1%
0.0225991
< 0.1%
0.0226541
< 0.1%
0.0226951
< 0.1%
0.0226971
< 0.1%
ValueCountFrequency (%)
708.7038471
< 0.1%
220.5674561
< 0.1%
121.8288361
< 0.1%
90.4261921
< 0.1%
83.8552851
< 0.1%
77.9031991
< 0.1%
43.2650031
< 0.1%
37.2090661
< 0.1%
36.0913221
< 0.1%
32.9013231
< 0.1%

rErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct143132
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1199264538
Minimum0.034156
Maximum39.832404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:05.166344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.034156
5-th percentile0.0458615
Q10.0625315
median0.100181
Q30.1510175
95-th percentile0.262194
Maximum39.832404
Range39.798248
Interquartile range (IQR)0.088486

Descriptive statistics

Standard deviation0.1420781739
Coefficient of variation (CV)1.184710874
Kurtosis26445.80792
Mean0.1199264538
Median Absolute Deviation (MAD)0.041204
Skewness115.6323666
Sum31836.9956
Variance0.0201862075
MonotonicityNot monotonic
2022-02-24T00:58:05.260095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05104413
 
< 0.1%
0.04416612
 
< 0.1%
0.05527612
 
< 0.1%
0.0527912
 
< 0.1%
0.05093611
 
< 0.1%
0.05686811
 
< 0.1%
0.05623211
 
< 0.1%
0.05022711
 
< 0.1%
0.05352911
 
< 0.1%
0.04638611
 
< 0.1%
Other values (143122)265356
> 99.9%
ValueCountFrequency (%)
0.0341561
< 0.1%
0.0344761
< 0.1%
0.0346441
< 0.1%
0.0346971
< 0.1%
0.0349011
< 0.1%
0.0350191
< 0.1%
0.0351191
< 0.1%
0.0352071
< 0.1%
0.0352651
< 0.1%
0.0352891
< 0.1%
ValueCountFrequency (%)
39.8324041
< 0.1%
22.3278851
< 0.1%
12.8581851
< 0.1%
12.2722541
< 0.1%
12.1865371
< 0.1%
10.613931
< 0.1%
9.5024491
< 0.1%
8.9521561
< 0.1%
8.8973921
< 0.1%
8.3730051
< 0.1%

iErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct136364
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1328689264
Minimum0.033318
Maximum66.143307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:05.353844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.033318
5-th percentile0.0575865
Q10.0750465
median0.10011
Q30.143197
95-th percentile0.306796
Maximum66.143307
Range66.109989
Interquartile range (IQR)0.0681505

Descriptive statistics

Standard deviation0.2164796617
Coefficient of variation (CV)1.629272302
Kurtosis35650.32194
Mean0.1328689264
Median Absolute Deviation (MAD)0.029815
Skewness137.6867747
Sum35272.84677
Variance0.04686344391
MonotonicityNot monotonic
2022-02-24T00:58:05.447594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07677512
 
< 0.1%
0.07013911
 
< 0.1%
0.07640211
 
< 0.1%
0.07165811
 
< 0.1%
0.07760311
 
< 0.1%
0.07518911
 
< 0.1%
0.07562111
 
< 0.1%
0.06071811
 
< 0.1%
0.06852411
 
< 0.1%
0.08239610
 
< 0.1%
Other values (136354)265361
> 99.9%
ValueCountFrequency (%)
0.0333181
< 0.1%
0.0395561
< 0.1%
0.0395691
< 0.1%
0.040541
< 0.1%
0.0414321
< 0.1%
0.0414431
< 0.1%
0.0415041
< 0.1%
0.0417341
< 0.1%
0.0417521
< 0.1%
0.0420231
< 0.1%
ValueCountFrequency (%)
66.1433071
< 0.1%
32.416251
< 0.1%
20.4407171
< 0.1%
20.0244661
< 0.1%
18.3347891
< 0.1%
17.4917231
< 0.1%
13.2475611
< 0.1%
11.9594821
< 0.1%
11.8463591
< 0.1%
11.371971
< 0.1%

zErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct171161
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2216285618
Minimum0.044069
Maximum47.529248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-02-24T00:58:05.525718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.044069
5-th percentile0.079096
Q10.109228
median0.148065
Q30.2278285
95-th percentile0.648622
Maximum47.529248
Range47.485179
Interquartile range (IQR)0.1186005

Descriptive statistics

Standard deviation0.2936028413
Coefficient of variation (CV)1.324751823
Kurtosis4242.307003
Mean0.2216285618
Median Absolute Deviation (MAD)0.048007
Skewness40.41128375
Sum58835.95594
Variance0.08620262843
MonotonicityNot monotonic
2022-02-24T00:58:05.619469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1152169
 
< 0.1%
0.1086539
 
< 0.1%
0.1140948
 
< 0.1%
0.1137518
 
< 0.1%
0.0886788
 
< 0.1%
0.106858
 
< 0.1%
0.0887938
 
< 0.1%
0.1286888
 
< 0.1%
0.1383798
 
< 0.1%
0.1026228
 
< 0.1%
Other values (171151)265389
> 99.9%
ValueCountFrequency (%)
0.0440691
< 0.1%
0.0441531
< 0.1%
0.0442971
< 0.1%
0.0446671
< 0.1%
0.0450211
< 0.1%
0.0454211
< 0.1%
0.0467181
< 0.1%
0.0470861
< 0.1%
0.047521
< 0.1%
0.0476191
< 0.1%
ValueCountFrequency (%)
47.5292481
< 0.1%
29.8753021
< 0.1%
28.7136821
< 0.1%
26.3650851
< 0.1%
23.7111791
< 0.1%
22.2426741
< 0.1%
19.0154721
< 0.1%
17.9289941
< 0.1%
17.2466541
< 0.1%
16.9436931
< 0.1%

Interactions

2022-02-24T00:58:00.716979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:38.277850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:40.277948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:42.331409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:44.491853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:46.531211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:48.534573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:50.590026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:52.677078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:54.670937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:56.595816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:58.727572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:00.869353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:38.444031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:40.449927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:42.514688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:44.663351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:46.689660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:48.700524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:50.771457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:52.838473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:54.840691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:56.756415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:58.903352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:01.024071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:38.656201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:40.621112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:42.690917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:44.828393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:46.879538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:48.866163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:50.961203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:53.000387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:55.010274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:57.000832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:59.077552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:01.175193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:38.827214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:40.782170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:42.862929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:45.007623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:47.052889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:49.037912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:51.112826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:53.150992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:55.171511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:57.160604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:59.240605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:01.331761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:38.986895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:40.937720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:43.043350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:45.167870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:47.226481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:49.262284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:51.277872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:53.301009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:55.325295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:57.331719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:59.406765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:01.478663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:39.143847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:41.160198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:43.220969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:45.338396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:47.393774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:49.403529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:51.437370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:53.460506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:55.479854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:57.479894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:59.644809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:01.622841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:39.300009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:41.321037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:43.383692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:45.500853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:47.571184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:49.556957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:51.610324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:53.616576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:55.636359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:57.675310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:59.790815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:01.780380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:39.474528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:41.504010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:43.562224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:45.663522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:47.748808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:49.739492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:51.780808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:53.787445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:55.801214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:57.866266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:59.956370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:01.929183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:39.631267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:41.658994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:43.808192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:45.821121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:47.899363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:49.899280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:52.003941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:53.943743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:55.956420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:58.016858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:00.109099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:02.147916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:39.794149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:41.853190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:43.970744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:45.980742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:48.061482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:50.086784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:52.172066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:54.126234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:56.113565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:58.208720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:00.262606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:02.315664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:39.961549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:42.011314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:44.158982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:46.143841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:48.220873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:50.253819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:52.359002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:54.287024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:56.283040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:58.381040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:00.419447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:02.474215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:40.123617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:42.171705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:44.315530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:46.300452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:48.393278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:50.440654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:52.513497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:54.521841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:56.440130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:57:58.557949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T00:58:00.569388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-24T00:58:05.697594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-24T00:58:05.806969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-24T00:58:05.978844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-24T00:58:06.088219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-24T00:58:02.597968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-24T00:58:02.849166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexIDugrizuErrgErrrErriErrzErr
00123764587956292880525.15537522.23298121.25784119.88985419.4271071.0346570.2190330.2069520.1473470.228686
11123764594290550404021.91066719.46443918.37297817.95550217.6429790.4438520.0606660.0648090.0699810.096798
22123764594290563518020.97867020.27682119.47230519.23270618.9941770.1501200.0708100.0768910.0855700.132459
33123764594290622486020.38998019.31364317.89161517.41506617.1812310.3316360.1132950.0923970.0931780.127449
44123764594397872152023.90716923.44867520.25651719.16386018.5571521.5279601.0334150.1774880.1328690.149485
55123764594397878713820.50301419.28054218.83047718.55928018.4818270.1202140.0447400.0615190.0719900.098771
66123764594397878721221.72803519.97735818.90186518.37909918.0371950.3158950.0619510.0635940.0686380.084922
77123764594397878723820.09530118.80882518.19224417.88397017.6554570.1393420.0546250.0730510.0855040.116468
88123764594397885252619.98365218.80582817.91803917.49641017.2386510.0928220.0422390.0540850.0625370.077156
99123764594397885286524.60208721.48808719.52091018.92142518.5942520.8667160.1796410.0909750.0914850.116313

Last rows

df_indexIDugrizuErrgErrrErriErrzErr
26546174631123767944042558273425.91736022.45076021.41907520.99893621.3901630.5691800.1888620.1630150.1788720.480358
26546274632123767944042558296523.63934722.19899921.03185320.62351620.2202761.1643130.2077200.1689910.1897670.281953
26546374633123767944042558299325.01959823.56868221.38697220.71624620.1403731.1668850.5109550.1810360.1706060.219201
26546474635123767944042558306524.12803622.61571922.08888422.17021822.7805810.8401290.1623390.1931050.2956700.616266
26546574636123767944042558332124.79755024.09209122.12824821.59173221.1697860.8273330.4694210.2019030.1988410.295564
26546674637123767944042558332924.97644824.60020122.03163921.27790621.7423590.9938660.7457570.2372570.2035700.556340
26546774638123767944042558335222.11512622.93945321.89254621.64350921.1614910.2979240.2879820.2382970.2872310.418816
26546874639123767944042558339325.05322323.42927221.97012121.79635421.7643320.9601380.3748650.2233370.2893260.562293
26546974640123767944042558340623.51894823.86224722.47657023.32341222.4735530.6650530.4265650.2710130.6746570.628833
26547074641123767944042558355223.85055523.66226822.62598421.67310320.7279070.9956840.4756500.3804950.2794380.296898